using OpenCVForUnity.CoreModule;
using OpenCVForUnity.DnnModule;
using OpenCVForUnity.ImgprocModule;
using OpenCVForUnity.ObjdetectModule;
using OpenCVForUnityExample.DnnModel;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Runtime.InteropServices;
using System.Text;
using UnityEngine;

public class MFacialExpressionRecognizer : FacialExpressionRecognizer
{

    List<Scalar> mPalette = new List<Scalar>();
    public MFacialExpressionRecognizer(string modelFilepath, string SF_modelFilepath, string SF_configFilepath, int backend = Dnn.DNN_BACKEND_OPENCV, int target = Dnn.DNN_TARGET_CPU):base(modelFilepath,SF_modelFilepath,SF_configFilepath,backend,target)
    {
        mPalette = new List<Scalar>();
        mPalette.Add(new Scalar(255, 56, 56, 255));
        mPalette.Add(new Scalar(82, 0, 133, 255));
        mPalette.Add(new Scalar(52, 69, 147, 255));
        mPalette.Add(new Scalar(255, 178, 29, 255));
        mPalette.Add(new Scalar(55, 55, 55, 255));
        mPalette.Add(new Scalar(100, 115, 255, 255));
        mPalette.Add(new Scalar(255, 112, 31, 255));
    }

    public FaceInfo[] visualize(Mat image, List<Mat> results, Mat faces, bool isRGB = false)
    {
        if (image.IsDisposed)
            return new FaceInfo[0];

        if (results.Count != faces.rows())
            return new FaceInfo[0];

        StringBuilder sb = null;

        //if (print_results)
        //    sb = new StringBuilder();

        FaceInfo[] faceInfos = new FaceInfo[results.Count];

        for (int i = 0; i < results.Count; ++i)
        {
            float[] face_box = new float[4];
            faces.get(i, 0, face_box);

            float left = face_box[0] + 2;
            float top = face_box[1] + 2;
            float right = face_box[0] + face_box[2] - 2;
            float bottom = face_box[1] + face_box[3] - 2;

            ClassificationData bmData = getBestMatchData(results[i]);
            int classId = (int)bmData.cls;
            string label = getClassLabel(bmData.cls) + ", " + String.Format("{0:0.0000}", bmData.conf);

            Scalar c = mPalette[classId % mPalette.Count];
            Scalar color = isRGB ? c : new Scalar(c.val[2], c.val[1], c.val[0], c.val[3]);

            // draw box
            Imgproc.rectangle(image, new Point(left, top), new Point(right, bottom), color, 2);

            // draw label
            int[] baseLine = new int[1];
            Size labelSize = Imgproc.getTextSize(label, Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, 1, baseLine);

            top = Mathf.Max((float)top, (float)labelSize.height);
            Imgproc.rectangle(image, new Point(left, top + 2),
                new Point(left + labelSize.width, top + labelSize.height + baseLine[0] + 2), color, Core.FILLED);
            Imgproc.putText(image, label, new Point(left, top + labelSize.height + 2), Imgproc.FONT_HERSHEY_SIMPLEX, 0.5, Scalar.all(255), 1, Imgproc.LINE_AA);

            FaceInfo info = new FaceInfo();
            info.expression = getClassExp(bmData.cls);
            info.confidence = bmData.conf;
            info.box = new UnityEngine.Rect(left, bottom, right - left, top - bottom);
            faceInfos[i] = info;
            // Print results
            //if (print_results)
            //{
            //    sb.AppendLine(String.Format("-----------expression {0}-----------", i + 1));
            //sb.AppendLine(String.Format("Best match: " + getClassLabel(bmData.cls) + ", " + bmData));
            //}
        }
        //if (print_results)
        //    Debug.Log(sb);

        return faceInfos;
    }
    public FaceInfo.Expression getClassExp(float id)
    {
        /*
         * ("angry");
            ("disgust");
            ("fearful");
            ("happy");
            ("neutral");
            ("sad");
            ("surprised");
         * */
        int index = (int)id;
        if(index >= 0)
        {
            switch (index)
            {
                case 0:
                    return FaceInfo.Expression.Angry;
                case 1:
                    return FaceInfo.Expression.Disgust;
                case 2:
                    return FaceInfo.Expression.Fearful;
                case 3:
                    return FaceInfo.Expression.Happy;
                case 4:
                    return FaceInfo.Expression.Neutral;
                case 5:
                    return FaceInfo.Expression.Sad;
                case 6:
                    return FaceInfo.Expression.Suprised;
                default:
                    return FaceInfo.Expression.Neutral;
            }
        }
        return FaceInfo.Expression.Neutral;
    }
}